System Architecture for VR Yoga Therapy Platform with 6-DoF Whole-Body Avatar Tracking

Abstract

Chronic pain is a leading cause of morbidity among children and adolescents affecting 35% of the global population. Pediatric chronic pain management requires integrative health methods spanning physical and psychological subsystems through various mind-body interventions. Yoga therapy is one such method, known for its ability to improve the quality of life both physically and psychologically in chronic pain conditions. However, maintaining the clinical outcomes of personalized yoga therapy sessions at-home is challenging due to fear of movement, lack of motivation, and boredom. Virtual Reality (VR) has the potential to bridge the gap between the clinic and home by motivating engagement and mitigating pain-related anxiety or fear of movement. We developed a multi-modal algorithmic architecture for fusing real-time 3D human body pose estimation models with custom developed inverse kinematics models of physical movement to render biomechanically informed 6-DoF whole-body avatars capable of embodying an individual's real-time yoga poses within the VR environment. Experiments conducted among control participants demonstrated superior movement tracking accuracy over existing commercial off-the-shelf avatar tracking solutions, leading to successful embodiment and engagement. These findings demonstrate the feasibility of rendering virtual avatar movements that embody complex physical poses such as those encountered in yoga therapy. The impact of this work moves the field one step closer to an interactive system to facilitate at-home individual or group yoga therapy for children with chronic pain conditions.

Keywords: Biomechanically Driven Avatars; Human computer interaction (HCI); Human-centered computing; Interaction paradigms; Pediatric Pain; VR Rehabilitation; Virtual reality; Yoga Therapy.

Figure 1.

Avatar Architecture Pipeline: After obtaining measure from a body tracking method, we validate our input using biomechanical constraints and refine the measure with a Kalman filter fusing the input with a generic hand tracking method to obtain a full body representation of an Avatar. Gray blocks indicate avatar rigging, amber indicates tracking modalities, blue indicates inverse kinematics and green indicates noise filtration algorithms.

Figure 2.

Illustration of joints and their associated tracking methods with two-bone IK chain. The end joint follows the same position as the target. The target being the input from the sensing modality.

Figure 3.

(top) WMPJPE over all joints comparing our multi-modal avatar to the raw Kinect avatar, (bottom) WMPJPE averaged across poses comparing our multi-modal avatar to the raw Kinect avatar.

Figure 4.

Avatar Unity renderings of floor (1.A) and standing (2.A) pose and their respective ground truth image for cobra floor pose (1.B) and tree standing pose (2.B).

Figure 5.

Virtual Embodiment Questionnaire (VEQ): questions (Left) and responses (Right) categorized as negative, positive, or neutral. Questions 1–4 (Ownership). Questions 4–8 (Agency)